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A NEW METHOD FOR DETECTION OF NOISE IN CORRUPTED IMAGE

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Available Online at www.ijpret.com 1350

INTERNATIONAL JOURNAL OF PURE AND

APPLIED RESEARCH IN ENGINEERING AND

TECHNOLOGY

A PATH FOR HORIZING YOUR INNOVATIVE WORK

A NEW METHOD FOR DETECTION OF NOISE IN CORRUPTED IMAGE

NIKHIL NALE1, ANKIT MUNE2

1.Computer Engineering Department, Student (M.E.), IBSS College of Engg. & Tech., Amravati, India. 2.Computer Engineering Department, Asst. Professor (CSE), IBSS College of Engg. &Tech, Amravati.

Accepted Date: 05/03/2015; Published Date: 01/05/2015

Abstract:Noise cannot be avoidable in communication networks, and its presence can affect the quality of image. The main approach of this paper is to detect the noise in corrupted image and try to reduce noise from image by using effective filtering technique. Given process is a two stage process in which first stage is used to detect the type of noise in the image such as impulse Gaussian noise etc. whereas second stage is used to reduce the noise from corrupted image. The main approach of this process is to improve the quality of image.

Keywords:Noise, Corrupted Image

Corresponding Author: MISS. NIKHIL NALE

Access Online On:

www.ijpret.com

How to Cite This Article:

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Available Online at www.ijpret.com 1351 INTRODUCTION

Noise can be present in the communication channel and have tremendous effect in image being sent. In this paper we proposed a new technique to detect and reduce a noise from degraded image. Images captured by digital cameras could be affected by noise due to random variations of pixel elements in the camera sensors. Noise represents unwanted information which harms image quality. Noise can be unavoidable in communication networks, and its presence can have terrible effects upon the data being sent. There are various types of image noise present in the image such as Gaussian noise, salt and pepper noise, random valued impulse noise, speckle noise, Uniform noise [6].

Salt and Pepper Noise:

Salt and Pepper noise is also known as Impulse Noise. This noise can be caused by sharp & sudden disturbances in the image signal. It represents itself is randomly occurring white or black (or both) dots over image.

Gaussian Noise

Gaussian Noise is caused by random fluctuations in the signal. It’s modeled by random values added to an image.

Speckle Noise

Speckle noise can be modeled by random values multiplied by pixel values of an image.

Uniform Noise

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Available Online at www.ijpret.com 1352 II Related Work

Many of the current papers dealing with noise in communication networks which propose a two stage method of impulse noise reduction where in the first stage noise is detected and in the second it compensated for a filtering technique.

As per Ming Yan’s paper [1] when the noise level is not high, adaptive center-weighted median filter (ACWMF) is appropriate method for removing random-valued impulse noise. Paper presents a general algorithm for blind image in painting and removing impulse noise by iteratively restoring the image and identifying the damaged pixels.

H. Hosseini and F. Marvasti’s [2] introduced GFN (General Fixed-Valued Impulse Noise) model. For the GFN model, An Impulse Value Detector (IVD) is required to determine the noise values. In this, received image is denoted as I and Image entropy is defined as, entropy

(I)=∑255𝑖=0𝑝𝑖 log 𝑝𝑖

Where, pi is the probability of the grey-value i and can be interpreted as the normalized histogram of the image. While the Gaussian noise does not affect the image entropy, the impulse noise significantly decreases it. The impulse value detector, iteratively, detects and removes the impulse grey-values. If the corresponding pixels have the lowest correlation with their neighbors then the grey value is detected as an impulse. After each iteration, when the impulse value is removed, the image entropy increases. The process continues until the entropy becomes larger than the entropy threshold, thus it ensures that there are no more impulse values in the image and image restoration is done by using AIM filter. In this filter, the noisy pixels which are farther than their nearest uncorrupted pixel, will be modified in more iterations.

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Available Online at www.ijpret.com 1353 Shyam Lal et. al. [4] introduced a methodology “Noise Removal Algorithm for Images Corrupted by Additive Gaussian Noise”, in which, two fundamental mathematical morphological operations such as dilation and erosion are used. Dilation adds pixels to the boundaries of objects in an image, whereas erosion removes pixels on object boundaries. Mathematical morphological operations are also useful in smoothing and sharpening.

Deborah D. et al [5] introduced an algorithm for Corrupted Images, in which first stage detect the type of noise and in second stage which type of filter is suitable for detected noise is given to eliminate the noise.

Gurmeet kaur and Rupinder Kaur’s [6] describe Image De-noising using Wavelet Transform and various filters, in which the preprocessing of image is done before image can be used in application by De-noising of image. De-noising is done by using filtering and wavelet based approach.

WORKING OF SYSTEM

Proposed Algorithm is a two stage process which determine three things,

1. Presence of noise

2. Type of noise such as impulse noise or Gaussian

3. The effective filtering method for removing noise

Following fig. shows the block diagram of proposed system

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Available Online at www.ijpret.com 1354 Following subsection describe the Adaptive noise detector and Adaptive pixel restorer,

a) Adaptive Noise Detector :

Purpose of adaptive noise detector is to detect the type of noise present in image such as Gaussian, salt and pepper etc. similar to [7]

Step 1: Obtain the image histogram H of the degraded image

Step2: Compute the vector D which is the difference between adjacent locations in the histogram array H.

Di = Hi+1 − Hi for all i=0,1…255

Step 3: Various boundary thresholds are set, according to the maxima values found in D.

Nature of noise is detected from location of maxima value. If noise is detected in first stage the NT that is Noise Type indicator is assign as a input to the second stage and corrupted pixel is mapped with the binary matrix[7].

b) Adaptive Pixel Restorer :

Adaptive Pixel Restorer is second stage of our proposed system which used to remove the noise from corrupted image by using filtering technique which is suitable for removing noise. If the NT Indicator has not been set, the “corrupted image”, x(n), is allowed to pass and if the NT Indicator is set, then this sets one of the various noise flags.

Again, similar to the second stage of [5]. The Adaptive Pixel Restorer searches through the Binary Map for pixels whose value is “1”. If the neighborhood vector of values is not set as null and instance is found, it searches noise flags that are set to determine what type of adaptive filtering is best for that instance which is corrupted. If the neighborhood vector of values in the vicinity of that particular pixel is null, the algorithm goes on to the next pixel value. This process is done repeatedly until the Binary Map is cleared of 1’s. [5]

Following fig (2) shows the original image fig (3) shows image corrupted by salt and pepper noise with histogram , fig(4) shows image corrupted by the Gaussian noise with

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Available Online at www.ijpret.com 1355 Figure (2). Original Image

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Available Online at www.ijpret.com 1356 Figure (4). Image corrupted by Gaussian Noise with Histogram

CONCLUSION

In this paper we determined an efficient noise detection technique which is used for detection and reduction of noise in corrupted image by and it also used to improved quality of image by using effective filtering technique.

The discussed process can be proposed into real-time applications due to the simplicity of the algorithm.

REFERENCE

1. Ming yan “Restoration of Images Corrupted by Impulse Noise using Blind Inpainting and l0 Norm”, November 7, 2011.

2. H. Hosseini, F. Marvasti, Senior Member, IEEE “Fast Impulse Noise Removal from highly corrupted image”, Advance Communication Research Institute (ACRI).

3. Qin Zhiyuan a, Zhang Weiqiang a, Zhang Zhanmu a, Wu Bing b, Rui Jie a,Zhu Baoshan “A Robust Adaptive Image Smoothing Algorithm”

4. Shyam Lal1, Mahesh Chandra2 and Gopal Krishna Upadhyay “Noise Removal Algorithm for Images Corrupted by Additive Gaussian Noise”, International Journal of Recent Trends in Engineering, Vol 2, No. 1, November 2009, pp. 199-206.

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Available Online at www.ijpret.com 1357 6. Gurmeet kaur Rupinder Kaur “Image De-noising using Wavelet Transform and various filters”, International journal of researcher in computer science Eissn 2249-8265 volume 2(2012) pp.15-21

7. [Indu S and Chaveli Ramesh 2007], “A Noise Fading Technique for Images Highly Corrupted

Figure

Figure (1). Block diagram of Proposed system[5]
Figure (3) Image corrupted by Salt and pepper noise histogram of salt and pepper noise
Figure (4). Image corrupted by Gaussian Noise with Histogram

References

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